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2006
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Adaptive Feature Integration for Segmentation of 3D Data by Unsupervised Density Estimation

10 years 11 months ago
Adaptive Feature Integration for Segmentation of 3D Data by Unsupervised Density Estimation
In this paper, a novel unsupervised approach for the segmentation of unorganized 3D points sets is proposed. The method derives by the mean shift clustering paradigm devoted to separate the modes of a multimodal density by using a kernel-based technique. Here, the attention is focused on the selection of the kernel bandwidth which typically strongly affects the level of accuracy of the segmentation results. In particular, a set of geometric features is computed from each 3D point of the given data. This set is projected onto a number of independent sub-spaces, each one associated to a different estimated feature, and overall forming a joint multidimensional (feature) space. In this space, we propose a method for selecting the best multidimensional kernel bandwidth in an automatic fashion, based on stability criteria. The final kernel considers each sub-space in an adaptive way in relation to the discrimination power of each feature, leading to accurate results when dealing with differ...
Marco Cristani, Umberto Castellani, Vittorio Murin
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Marco Cristani, Umberto Castellani, Vittorio Murino
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